In general, the translations in the two types were adapted to the use in different regions.

10% (15/147) and 8% (12/143) of the translations in types organization and others, respectively, had regional variations, because most of the terms in type others were general terms such as "bank" and "movies" and in type organization many local companies in Taiwan had no translation variations in mainland China and HK.


In PCFGs and PPDAs, probabilities are assigned to rules or transitions, respectively.

these probabilities cannot be chosen entirely arbitrarily.


It is not difficult to see that there exists a bijection f from complete computations of A to complete derivations of G , and that we have for each complete computation c. Thus (G , p G ) is consistent.


As discussed in the introduction, the properness condition reduces the number of parameters of a PPDT.

our results show that if the PPDT has the CPP and the SPP then the properness assumption is not restrictive, i.e., by lifting properness we do not gain new distributions with respect to those induced by the underlying PCFG.


For a total of 454 questions (91%), a simple reciprocal constraint could be generated.

for 61 of those, the reciprocal question was sufficiently non-specific that the sought reciprocal answer was unlikely to be found in a reasonably-sized hit-list.


Altogether, we see that some of the very problems we aimed to skirt are still present and need to be addressed.

we have shown that even disregarding these issues, QDC was able to provide substantial improvement in accuracy.


These parameters can be easily determined in PDT, as each incoming case is classified into each class with a probability.

the incoming cases in NBC are grouped into one class which is assigned the highest score.


Our informativeness-based measure is similar to these works.

these works just follow a single criterion.


Moreover, the representativeness measure we use is relatively general and easy to adapt to other tasks, in which the example selected is a sequence of words, such as text chunking, POS ta gging, etc.

(Brinker 2003) first incorporate diversity in active learning for text classification.


Their work is similar to our local consideration in Section 2.3.2.

he didn' t further explore how to avoid selecting outliers to a batch.


the gunman kill police As we have shown earlier, BLEU-2 cannot differentiate S2 from S3.

s2 has a ROUGE-L score of 3/4 = 0.75 and s3 has a ROUGE-L score of 2/4 = 0.5, with β = 1.


The Pearson's ρ correlation values in the Stem set of the Fluency Table, indicates that BLEU12 has the highest correlation (0.93) with fluency.

it is statistically indistinguishable with 95% confidence from all other metrics shown in the Case set of the Fluency Table except for WER and  GTM10.


It can be seen from the table that there is a strong positive correlation between the baseline BLEU scores and human scores for Fluency: r(2)=0.9807, p <0.05.

the correlation with Adequacy is much weaker and is not statistically significant: r(2)= 0.5918, p >0.05.


Future work will apply our approach to evaluating MT into languages other than English, extending the experiment to a larger number of MT systems built on different architectures and to larger corpora.

the results of the experiment may also have implications for MT development: significance weights may be used to rank the relative "importance" of translation equivalents.


Almost all of sense disambiguation methods are heavily dependant on manually compiled lexical resources.

these lexical resources often miss domain specific word senses, even many new words are not included inside.


The naïve Bayes model was found to be the most accurate classifier in a comparative study using a subset of Senseval-2 English lexical sample data by Yarowsky and Florian (2002).

the maximum entropy (Jaynes, 1978) was found to yield higher accuracy than naïve Bayes in a subsequent comparison by Klein and Manning (2002), who used a different subset of either Senseval-1 or Senseval-2 English lexical sample data.


The label and string position method is useful if one sees the task as inserting empty nodes into a string, and thus is quite useful for evaluating systems that detect empty categories without parse trees, as in Dienes and Dubey (2003a).

if the task is to insert empty nodes into a tree, then the method leads both to false positives and to false negatives.


If a system places the trace in position 2 instead, the string position method will count it as an error, since 1 and 2 have different string positions.

it is not at all clear what it means to say that one of those two positions is correct and the other not, since there is no semantic, grammatical, or textual indicator of its exact position.


The items of Translator CT have a -dimensional label vector, as usual.

their d-span vectors are only -dimensional, because it is not necessary to constrain absolute word positions in the output dimensions.


The move to MCSG is motivated by our desire to more perspicuously account for certain syntactic phenomena that cannot be easily captured by context-free grammars, such as clitic climbing, extraposition, and other types of longdistance movement (Becker et al., 1991).

mCSG still observes some restrictions that make the set of languages it generates less ex-pensive to analyze than the languages generated by (properly) context-sensitive formalisms.


A CFG can always be binarized into another CFG: two adjacent nonterminals are replaced with a single nonterminal that yields them.

it can be impossible to binarize a -GMTG into an equivalent -GMTG¨ .


Until recently, HMMs were the predominant formalism to model label sequences.


In general, labelers followed the basic conventions of EToBI for coding (Taylor, 2000).


So far, all of our predictors are ones easily computed from a string of text.

we have included a few variables that affect the likelihood of a word being accented that require some acoustic data.


Given the distinct natures of corpora used, it is difficult to compare these results with earlier models.

in experiment 1 (HMM: POS, Unigram vs CRF: POS, Unigram) we have shown that a CRF model achieves a better performance than an HMM model using the same features.


However, in experiment 1 (HMM: POS, Unigram vs CRF: POS, Unigram) we have shown that a CRF model achieves a better performance than an HMM model using the same features.

the real strength of CRFs comes from their ability to incorporate different sources of information efficiently, as is demonstrated in our experiments.


We did not test directly the probabilistic measures (or collocation measures) that have been used before for this task, namely information content (IC) (Pan and McKeown, 1999) and mutual information (Pan and Hirschberg, 2001).

the measures we have used encompass similar information.


The disjunctive discourse marker or is also NON-VERIDICAL, because it does not imply that both of its arguments are true.

and does imply this, and so has the feature veridical-ity=VERIDICAL.


For Spanish CallHome, (Ries, 1999) reports 76.2% accuracy with a hybrid approach that couples Neural Networks and ngram backoff modeling; the former uses prosodic features and POS tags, and interestingly works best with unigram backoff modeling, i.e., without taking into account the DA history -see our discussion of the ineffectiveness of the DA history below.

(Ries, 1999) does not mention 4 The baselines for CallHome37 and CallHome10 are the same because in both statement is the most frequent DA.


We choose this task because the original intention of this shared task was to test the effectiveness of semi-supervised learning methods.

it turned out that none of the top performing systems used unlabeled data.


Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks.

these tasks have mostly involved very small label sets.


CRFs are usually estimated using gradient-based methods such as limited memory variable metric (LMVM).

even with these efficient methods, training can be slow.


Evaluating the first term of the derivative is quite simple.

the sum over all possible labellings in the second term poses more difficulties.


When using a very short code, the error-correcting CRF will not adequately model the decision boundaries between all classes.

using a long code will lead to a higher degree of dependency between pairs of classifiers, where both model similar concepts.


Using such a large number of weak learners is costly, in this case taking roughly ten times longer to train than the multiclass CRF.

much shorter codes can also achieve similar results.


Specificity ordering is a necessary step for building a noun hierarchy.

this approach clearly cannot build a hierarchy alone.


The interaction between the head verb and the preposition determine whether the noun is an indirect object of a ditransitive verb or alternatively the head of a PP that is modifying the verb.

sEXTANT always attaches the PP to the previous phrase.


This technique is similar to Hearst and Schütze (1993) and Widdows (2003).

sometimes the unknown noun does not appear in our 2 billion word corpus, or at least does not appear frequently enough to provide sufficient contextual information to extract reliable synonyms.


We have also experimented with filtering out highly polysemous nouns by eliminating words with two, three or more synonyms.

such a filter turned out to make little difference.


We overcome this by using the synonyms of the last word in the multi-word term.

there are 174 multi-word terms (23%) in the WORDNET 1.7.1 test set which we could probably tag more accurately with synonyms for the whole multi-word term.


Our WSD system uses heuristics to attempt to detect predicate arguments from parsed sentences.

recognition of predicate argument structures is not straightforward, because a natural language will have several different syntactic realizations of the same predicate argument relations.


Accuracy orig 0.564 orig*fset 0.587 orig+pb 0.597 (orig+pb)*fset 0.628 Table 5: Accuracy of system on WordNet sensetagging of 20 Senseval-2 verbs with more than one frameset, with and without gold-standard frameset tag.

partitioning the instances using the automatically generated frameset tags has no significant effect on the system's performance; the information provided by the automatically assigned coarse-grained sense tag is already encoded in the features used for fine-grained sense-tagging.


Regarding the role of NL interfaces for ITSs, only very recently have the first few results become available, to show that first of all, students do learn when interacting in NL with an ITS (Litman et al., 2004;Graesser et al., 2005).

there are very few studies like ours, that evaluate specific features of the NL interaction, e.g.


In the ideal situation, the generator would have produced texts with the perfect regression scores and they would be identical to the stylistic scores, so the graph in the figure 9 would be like a gridshape one as in figure 6.

we have already seen in figure 7, that this is not the case for the relation between the target coordinates and the regression scores.


The disadvantage of this is that variation is not grounded in some 'intuitive' notion of style: the interpretation of the stylistic dimensions is subjective and tentative.

as no comprehensive computationally realisable theory of style yet exists, we believe that this approach has considerable promise for practical, empirically-based stylistic control.


IDL-expressions are a convenient way to compactly represent finite languages.

iDLexpressions do not directly allow formulations of algorithms to process them.


The results shown in Figure 7 suggest that we could further improve parsing performance by increasing the model size.

both the memory size and the training time are more than linear in vH w v , and the training time for the largest ( ) models was about 15 hours for the models created using CENTER-PARENT, CENTER-HEAD, and LEFT and about 20 hours for the model created using RIGHT.


The above method allows for the tractable estimation of log-linear models on exponentially-many HPSG parse trees.

despite the development of methods to improve HPSG parsing efficiency (Oepen et al., 2002a), the exhaustive parsing of all sentences in a treebank is still expensive.


Actually, in our previous study (Miyao et al., 2003), we successfully developed a probabilistic model including features on nonlocal predicate-argument dependencies.

since we could not observe significant improvements by incorporating nonlocal features, this paper investigates only the features described above.


Table 4 revealed that our simple method of filtering caused a fatal bias in training data when a preliminary distribution was used only for filtering.

the model combined with a preliminary model achieved sufficient accuracy.


In contrast, RULE, SYM, and LE features did not affect the accuracy.

if each of them was removed together with another feature, the accuracy decreased drastically.


Eisner and Satta (1999) give a cubic algorithm for lexicalized phrase structures.

it only works for a limited class of languages in which tree spines are regular.


However, they are fundamentally limited by their approximate search algorithm.

our system searches the entire space of dependency trees and most likely benefits greatly from this.


As shown in Table 3, the proportion of sentences containing some non-projective dependency ranges from about 15% in DDT to almost 25% in PDT.

the overall percentage of non-projective arcs is less than 2% in PDT and less than 1% in DDT.


It is likely that the more complex cases, where path information could make a difference, are beyond the reach of the parser in most cases.

if we consider precision, recall and Fmeasure on non-projective dependencies only, as shown in Table 6, some differences begin to emerge.


The most informative scheme, Head+Path, gives the highest scores, although with respect to Head the difference is not statistically significant, while the least informative scheme, Path -with almost the same performance on treebank transformation -is significantly lower (p < 0.01).

given that all schemes have similar parsing accuracy overall, this means that the Path scheme is the least likely to introduce errors on projective arcs.


They define the feature recall of w with respect to v as the weighted proportion of features of v that also appear in the vector of w. Then, they suggest that a hypernym would have a higher feature recall for its hyponyms (specifications), than vice versa.

their results in predicting the hyponymy-hyperonymy direction (71% precision) are comparable to the naïve baseline (70% precision) that simply assumes that general words are more frequent than specific ones.


According to Hypothesis I we expect that a pair (w, v) that satisfies entailment will also preserve feature inclusion.

by Hypothesis II if all the features of w are included by v then we expect that w entails v. We observed that Hypothesis I is better attested by our data than the second hypothesis.


At the word level we observed 14% invalid pairs of the first case and 30% of the second case.

our manual analysis shows, that over 90% of the first case pairs were due to a different sense of one of the entailing word, e.g.


Precision was significantly improved, filtering out 60% of the incorrect pairs.

the relative recall (considering RFF recall as 100%) was only reduced by 13%, consequently leading to a better relative F1, when considering the RFF-top-40 output as 100% recall (Table 5).


Clearly, OVA makes no explicit use of pairwise label or item relationships.

it can perform well if each class exhibits sufficiently distinct language; see Section 4 for more discussion.


One possible interpretation is that the relevant structure of the problem is already captured by linear regression (and perhaps a different kernel for regression would have improved its three-class performance).

according to additional experiments we ran in the four-class situation, the test-set-optimal parameter settings for metric labeling would have produced significant improvements, indicating there may be greater potential for our framework.


If we had a large collection of sensetagged text, then we could extract disambiguated feature vectors by collecting co-occurrence features for each word sense.

since there is little sense-tagged text available, the feature vectors for a random WordNet concept would be very sparse.


Clearly, when the system returns at most one or two attachments, the recall on the polysemous nodes is lower than on the Full set.

it is interesting to note that recall on the polysemous nodes equals the recall on the Full set after K=3.


We do not assume that local coherence is sufficient to uniquely determine the best ordering -other constraints clearly play a role here.

we expect that the accuracy of a coherence model is reflected in its performance in the ordering task.


For example, if two discourse connectives are SYNONYMS then we would expect them to have similar distributions.

if two connectives are EXCLUSIVE, then we would expect them to have dissimilar distributions.


A distributional similarity function provides only a one-dimensional comparison of two distributions, namely how similar they are.

we can obtain an additional perspective by using a variancebased function.


The key features of this approach are: Minimal human decision making.

to the standard approach, our method obviates, to a large extent, the need to make tough or potentially suboptimal design decisions.


With a beam width of 1000 the parser returns something like a 50-best list (Collins, personal communication), but the actual number of parses returned for each sentences varies.

turning off dynamic programming results in a loss in efficiency.


Dynamic programming parsing algorithms for PCFGs require O(m 2 ) dynamic programming states, where m is the length of the sentence, so an n-best parsing algorithm requires O(nm 2 ).

things get much worse when the grammar is bilexicalized.


To solve the problem efficiently, we now adopt a variant of the branch-and-bound algorithm, similar to that described in (Kudo and Matsumoto, 2004)  Abe and Zaki independently proposed an efficient method, rightmost-extension, for enumerating all subtrees from a given tree (Abe et al., 2002;Zaki, 2002).

first, the algorithm starts with a set of trees consisting of single nodes, and then expands a given tree of size (n−1) by attaching a new node to it to obtain trees of size n. it would be inefficient to expand nodes at arbitrary positions of the tree, as duplicated enumeration is inevitable.


The implicit calculation yields a practical computation in training.

in testing, kernel methods require a number of kernel evaluations, which are too heavy to allow us to realize real applications.


select relevant subtrees and achieves the best results for WSJ parsing.

these techniques are not based on the regularization framework focused on this paper and do not always eliminate all the redundant subtrees.


For these purposes, and because of its higher content validity, IPSyn scores often tells us more than MLU scores.

the MLU holds the advantage of being far easier to compute.


If we were looking for a fronted subordinate clause, only (c) would be a match.

each one of the sentences has an identical part-speechsequence.


Something like this approach seems to be adopted in the literature on communication between autonomous software agents.

even though many situations considered in multiagent systems do involve more than two agents, most interaction protocols are designed only for two participants at a time.


On the other hand, the contextual updates it enforces will not enable it to deal with the following (constructed) variant on 4, in other words does not afford responders to comment on previous responders, as opposed to the original querier: One arguable problem with this protocol-equally applicable to the corresponding DRed grounding protocol-is that it licences long distance acceptance and is, thus, inconsistent with the MAG benchmark.

it is potentially useful for interactions where there is explicitly more than one direct addressee.


S is proposing activity α L is considering proposal α Intention S is signalling that p L is recognizing that p Signal S is presenting signal σ L is identifying signal σ Channel S is executing behavior β L is attending to behavior β Table 1: Four levels of grounding Schlangen, 2004).

only the work of Purver (2004) addresses the question of how the source of the error affects the form the CR takes.


For example, common adjective-noun alternations are memorized.

since this linguistic information is not encoded in the model, unseen adjective noun pairs may still be handled incorrectly.


A simpler alternate approach would be to compare bags-ofwords.

since our possible orderings are bound by the induced tree structure, we might overzealously prune a candidate with a different tree structure that allows a better target order.


One might assume a smooth transition from text-based summarization to email and chat-based summarizations.

chat falls in the genre of correspondence, which requires dialogue and conversation analysis.


The conventional wisdom since Magerman (1995) has been that lexicalization substantially improves performance compared to an unlexicalized baseline model (e.g., a probabilistic context-free grammar, PCFG).

this has been challenged by Klein and Manning (2003), who demonstrate that an unlexicalized model can achieve a performance close to the state of the art for lexicalized models.


For example, (5) would be contracted to (4).

this approach only works if we are certain that the model is tagging the right words as compounds.


The impact of perfect tagging is less drastic on the lexicalized models (around 1% increase).

our main finding, viz., that lexicalized models outperform unlexicalized models considerable on the FTB, remains valid, even with perfect tagging.


Whenever the subject appears after the verb, the non-standard position may be annotated using a long-distance dependency (LDD).

as mentioned above, this information can also be retrieved from the grammatical function of the respective noun phrases: the GFs of the two NPs above would be 'subject' and 'accusative object' regardless of their position in the sentence.


NP case German articles and pronouns are strongly marked for case.

the grammatical function of all articles is usually NK, meaning noun kernel.


This is in part for the sake of simplicity: unlexicalized grammars are interesting because they are simple to estimate and parse, and adding smoothing makes both estimation and parsing nearly as complex as with fully lexicalized models.

because lexicalization adds little to the performance of German parsing models, it is therefore interesting to investigate the impact of smoothing on unlexicalized parsing models for German.


As both the Witten-Bell and Kneser-Ney variants are fairly well known, we do not describe them further.

as Brants' approach (to our knowledge) has not been used elsewhere, and because it needs to be modified for our purposes, we show the version of the algorithm we use in Figure 1.


The simplest kind of annotation is positional in nature, such as the association of a part-of-speech tag with each corpus position.

structural annotation such as that used in syntactic treebanks (e.g., Marcus et al., 1993) assigns a syntactic category to a contiguous sequence of corpus positions.


The chances of one of the middle 32 elements matching something in the internal context of the VP is relatively high, and indeed the twenty-sixth word is ein.

if we move stepwise out from the nucleus in order to try to match was ein Seeufer werden, the only options are to find ein directly to the right of was or Seeufer directly to the left of werden, neither of which occurs, thus stopping the search.


With s i , s i−1 we can further reduce the error rate to 20.26%.

when we add a third dependency, the error rate worsens to 29.32%, which indicates a number of parameters too high for the given amount of training data.


Clearly, Max-Ent is a rather different type of model from Stochastic OT, not only in the use of constraint ordering, but also in the objective function (conditional likelihood rather than likelihood/posterior).

it may be of interest to compare these two types of models.


But this may not be easy, due to the difficulty in likelihood evaluation (including likelihood ratio) discussed in Section 2.

our algorithm provides a general solution to the problem of learning Stochastic OT grammars.


Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.

one can dramatically improve the quality of the learned structure by exploiting simple prior knowledge of the desired solutions.


General, unconstrained induction of HMMs using the EM algorithm fails to detect useful field structure in either domain.

we demonstrate that small amounts of prior knowledge can be used to greatly improve the learned model.


This is the primary way supervised methods work, with the loss function relativized to training label patterns.

for unsupervised learning, the primary candidate for an objective function is the data likelihood, and we don't have another suggestion here.


The measure in Equation 1 is similar to the cosine metric, commonly used to determine the similarity of documents in the vector space model approach to Information Retrieval.

the cosine metric will not perform well for our application since it does not take into account the similarity between elements of a vector and would assign equal similarity to each pair of patterns in the example shown in Figure 1.


It is often simply an unstated assumption that any full translation system, to achieve full performance, will sooner or later have to incorporate individual WSD components.

in some translation architectures and particularly in statistical machine translation (SMT), the translation engine already implicitly factors in many contextual features into lexical choice.


This would give an upper-bound on performance and would quantify the effect of WSD errors.

we do not have a corpus which contains both sense annotation and multiple reference translations: the MT evaluation corpus is not annotated with the correct senses of Senseval target words, and the Senseval corpus does not include English translations of the sentences.


In these cases, some of the reference translations also use "impact".

even when the WSD model constrains the decoder to select "impact" rather than "shock", the resulting sentence translation yields a lower BLEU score.


(2004), or alternatively they could be incorporated as features for reranking in a maximum-entropy SMT model (Och and Ney, 2002), instead of using them to constrain the sentence translation hypotheses as done here.

the preceding discussion argues that it is doubtful that this would produce significantly different results, since the inherent problem from the "language model effect" would largely remain, causing sentence translations that include the WSD's preferred lexical choices to be discounted.


However this is clearly unfeasible for all-words WSD tasks, in which all the words of an open text should be disambiguated.

the word expert approach works very well for lexical sample WSD tasks (i.e.


Regarding the second direction, external knowledge would be required to help WSD algorithms to better generalize over the data available for training.

most of the state-of-the-art supervised approaches to WSD are still completely based on "internal" information only (i.e.


Algorithms based solely on deeper representations inevitably suffer from the errors in computing these representations.

low level processing such as tokenization will be more accurate, and may also contain useful information missed by deep processing of text.


(2000) used statistical parsing models to extract relational facts from text, which avoided pipeline processing of data.

their results are essentially based on the output of sentence parsing, which is a deep processing of text.


Using the notation just defined, we can write the two surface kernels as follows: 1) Argument kernel  Intuitively the dependency path connecting two arguments could provide a high level of syntactic regularization.

a complete match of two dependency paths is rare.


While short-distance relations dominate and can be resolved by above simple features, the dependency tree and parse tree features can only take effect in the remaining much less long-distance relations.

full parsing is always prone to long distance errors although the Collins' parser used in our system represents the state-of-the-art in full parsing.


We have thus removed the queries which contained OOV words -resulting in a set of 96 queries -which clearly biases the evaluation.

the results on both the 1-best and the lattice indexes are equally favored by this.


The prosody model is also affected, since the alignment of incorrect words to the speech is imperfect, thereby degrading prosodic feature extraction.

the prosody model is more robust to recognition errors than textual knowledge, because of its lesser dependence on word identity.


For log-linear models, POS information and an additional dictionary are used, which is not the case for GIZA++/IBM models.

treated as a method for performing symmetrization, log-linear combination alone yields better results than intersection, union, and refined methods.


1 A cept is defined as the set of target words connected to a source word (Brown et al., 1993).

both model 3 and model 4 do not take the multiword cept into account.


From the results, it can be seen that the larger the size of in-domain corpus is, the smaller the alignment error rate is.

when the number of the sentence pairs increase from 3030 to 5046, the error rate reduction in Table 4 is very small.


From the results in Table 6, it can be seen that the larger the size of out-of-domain corpus is, the smaller the alignment error rate is.

when the number of the sentence pairs is more than 130,000, the error rate reduction is very small.


As shown by the numbers in Table 1, the full lexicalized model produced promising alignment results on sentence pairs that have no more than 15 words on both sides.

due to its prohibitive O(n 8 ) computational complexity, our C++ implementation of the unpruned lexicalized model took more than 500 CPU hours, which were distributed over multiple machines, to finish one iteration of training.


To verify the safety of the tic-tac-toe pruning technique, we applied it to the unlexicalized ITG using the same beam ratio (10 −5 ) and found that the AER on the test data was not changed.

whether or not the top-k lexical head pruning technique is equally safe remains a question.


We construct a background model which is a basic unigram language model, P (A 2 ) = a∈A 2 P (a).

we then pick targets chosen by the confidence estimate this confidence estimate does not workwell in our dataset.


The experimental setup used in the fusion experiments was the same as before: training on 15 people, and testing on 137 people.

the postfusion evaluation differs from the pre-fusion evaluation.


Both kinds of models have been developed for tagging entities such as people, places and organizations in news material.

the rapid development of bioinformatics has recently generated interest on the extraction of biological entities such as genes (Collier et al., 2000) and genomic variations (McDonald et al., 2004b) from biomedical literature.


For the sentence "John Smith is the CEO at Inc. Corp.", the system would ideally extract the tuple (John Smith, CEO, Inc. Corp.).

for the sentence "Everyday John Smith goes to his office at Inc. Corp.", the system would extract (John Smith, ⊥, Inc. Corp.), since there is no mention of a job title.


If this instance is marked as negative, then the model might incorrectly disfavor features that correlate John to Inc. Corp..

if this instance is labeled positive, then the model may tend to prefer the shorter and more compact incomplete relations since they will be abundant in the positive training examples.


In the simplest version, these are simply treated like other constituents in the parse tree.

these can disrupt what may be termed the intended sequence of syntactic categories in the utterance, so we also tried skipping these constituents when mapping from the parse tree to shallow parse sequences.


These cues include phonology, prosody, morphology, and syntax in the context of an utterance.

global phonotactic cues at the level of utterance or spoken document remains unexplored in previous work.


Almost any tree-transduction operations defined on a single node will fail to generate the target sentence from the source sentence without using insertion/deletion operations.

if we view each dependency tree as an assembly of indivisible sub-sentential elementary trees (ETs), we can find a proper way to transduce the input tree to the output tree.


Such broken and crossing dependencies can be modeled by SDIG if they appear inside a pair of elementary trees.

if they appear between the elementary trees, they are not compatible with the isomorphism assumption on which SDIG is based.


It may be because the translation ambiguities of the chunk-based models are lower than those of the word-based models.

the processing speed of the IBM-style models is faster than the proposed model.


The use of bilingual verbnoun collocations is also useful for improving the performance.

we still have some problems of the data sparseness and the low coverage of bilingual verbnoun collocation.


The method works by examining one sample at a time, and makes an update , then the perceptron method can find such a separator.

it is not entirely clear what this method does when the training data are not completely separable.


Similarly, output like S2, despite its grammatical correctness, is choppy and too tedious to read.

our instance-based sentence boundary determination module will use examples in a corpus to partition those attributes into several sentences in a more balanced manner (S3).


Recall that the Yamcha classifiers are trained on TR1; in addition, Rip is trained on the output of these Yamcha clas-  The difference in performance between TE1 and TE2 shows the difference between the ATB1 and ATB2 (different source of news, and also small differences in annotation).

the results for Rip show that retraining the Rip classifier on a new corpus can improve the results, without the need for retraining all ten Yamcha classifiers (which takes considerable time).


We intend to apply our approach to Arabic dialects, for which currently no annotated corpora exist, and for which very few written corpora of any kind exist (making the dialects bad candidates even for unsupervised learning).

there is a fair amount of descriptive work on dialectal morphology, so that dialectal morphological analyzers may be easier to come by than dialect corpora.


Classification performance using Charniak parses is about 3% absolute worse than when using Tree-Bank parses.

argument identification performance using Charniak parses is about 12.7% absolute worse.


Since we used the whole of the German-English section of the Europarl corpus, we could not try improving the alignments by simply adding more German-English training data.

there is nothing that limits our paraphrase extraction method to drawing on candidate paraphrases from a single target language.


As we generate more number of random vectors, we can estimate the cosine similarity between two vectors more accurately.

in practice, the number (d) of random vectors required is highly domain dependent, i.e., it depends on the value of the total number of vectors (n), features (k) and the way the vectors are distributed.


Overall, the algorithm takes O(nk + nlogn) time.

for noun clustering, we generally have the number of nouns, n, smaller than the number of features, k.


For example, generating 10 random vectors gives us a cosine error of 0.4432 (which is a large number since cosine similarity ranges from 0 to 1.)

generation of more random vectors leads to reduction in error rate as seen by the values for 1000 (0.0493) and 10000 (0.0156).


We could not calculate the total time taken to build noun similarity list using the traditional technique on the entire corpus.

we estimate that its time taken would be at least 50,000 hours (and perhaps even more) with a few of Terabytes of disk space needed.


In recent years, researchers have proposed several algorithms to generate word alignments.

evaluating word alignments is difficult because even humans have difficulty performing this task.


Other metrics assess the impact of alignments externally, e.g., different alignments are tested by comparing the corresponding MT outputs using automated evaluation metrics (e.g., BLEU (Papineni et al., 2002) or METEOR (Banerjee and Lavie, 2005)).

these studies showed that AER and BLEU do not correlate well (Callison-Burch et al., 2004;Goutte et al., 2004;Ittycheriah and Roukos, 2005).


Similarly, if there is a missing link, only the recall is reduced slightly.

when computing CPER, an incorrect or missing alignment link might result in more than one phrase pair being eliminated from or added to the set of phrases.


While we expect some correlation between these two types of segmentation, they are clearly different problems.

one comparable study is described in (Galley et al., 2003).


Note that they improved their accuracy by combining the unsupervised output with discourse features in a supervised classifier -while we do not attempt a similar comparison here, we expect a similar technique would yield similar segmentation improvements.

we take a generative approach, modelling the text as being generated by a sequence of mixtures of underlying topics.


The focus of our work, however, is on an orthogonal yet fundamental aspect of this analysis -the impact of long-range cohesion dependencies on segmentation performance.

to previous approaches, the homogeneity of a segment is determined not only by the similarity of its words, but also by their relation to words in other segments of the text.


We aim to minimize the cut, which is defined to be the sum of the crossing edges between the two sets of nodes.

in other words, we want to split the sentences into two maximally dissimilar classes by choosing A and B to minimize: we need to ensure that the two partitions are not only maximally different from each other, but also that they are themselves homogeneous by accounting for intra-partition node similarity.


A k form a partition of the graph, and V −A k is the set difference between the entire graph and partition k. Decoding Papadimitriou proved that the problem of minimizing normalized cuts on graphs is N P -complete (Shi and Malik, 2000).

in our case, the multi-way cut is constrained to preserve the linearity of the segmentation.


In the formulation above we use sentences as our nodes.

we can also represent graph nodes with non-overlapping blocks of words of fixed length.


The noun-pronoun path coreference can be used directly as a feature in a pronoun resolution system.

path coreference is undefined for cases where there is no path between the pro-noun and the candidate noun -for example, when the candidate is in the previous sentence.


These features are calculated by mining the parse trees, and then could be used for resolution by using manually designed rules (Lappin and Leass, 1994;Kennedy and Boguraev, 1996;Mitkov, 1998), or using machine-learning methods (Aone and Bennett, 1995;Yang et al., 2004;Luo and Zitouni, 2005).


Normally, parsing is done on the sentence level.

in many cases a pronoun and an antecedent candidate do not occur in the same sentence.


This should be because feature Full-Expansion captures a larger portion of the parse trees, and thus can provide more syntactic information than Min Expansion or Simple Expansion.

if the texts are less-formally structured as those in BNews, Full-Expansion would inevitably involve more noises and thus adversely affect the resolution performance.


As shown in Table 5, the two grammaticalrole features are important for the pronoun resolution: removing these features results in up to 5.7% (NWire) decrease in success.

when the structured feature is included, the loss in success reduces to 1.9% and 1.1% for NWire and BNews, and a slight improvement can even be achieved for NPaper.


In line with the reports in (Luo and Zitouni, 2005) we do observe the performance improvement against the baseline (NORM) for all the domains.

the increase in the success rates (up to 1.3%) is not so large as by adding the structured feature (NORM+S Simple) instead.


We can see that Charniak (2000)'s parser leads to higher success rates for NPaper and BNews, while 's achieves better results for NWire.

the difference between the results of the two parsers is not significant (less than 2% success) for the three domains, no matter whether the structured feature is used alone or in combination.


Traditionally, syntactic information from parse trees is represented as a set of flat features.

the features are usually selected and defined by heuristics and may not necessarily capture all the syntactic information provided by the parse trees.


In the psycholinguistics literature, the comparative difficulty of object-relative clauses has been explained in terms of verbal working memory (King and Just, 1991), distance between the gap and the filler (Bever and McElree, 1988), or perspective shifting (MacWhinney, 1982).

the test results in this study provide a simpler account for the effect.


Corley and Crocker clearly state that their model is strictly limited to lexical ambiguity resolution, and their test of the model was bounded to the noun-verb ambiguity.

the findings in the current study play out differently.


In this conception, a language (be it natural or not) is produced (or generated) by a grammar by means of a specific mechanism, for example derivation.

when no structure can be built, nothing can be said about the input to be parsed except, eventually, the origin of the failure.


This information is computed as follows: Let C a construction defined in the grammar by means of a set of properties S C , let A C an assignment for the construction C, • Satisfaction ratio (SR): the number of satisfied properties divided by the number of eval- The SR value varies between 0 and 1, the two extreme values indicating that no properties are satisfied (SR=0) or none of them are violated (SR=1).

sR only relies on the evaluated properties.


Let's imagine the case where 7 constraints can be evaluated for both constructions, with an equal SR.

the two constructions do not have the same quality: one relies on the evaluation of all the possible constraints (in the PP) whereas the other only uses a few of them (in the VP).


The experiment described in the next section will show that this weighting level seems to be efficient enough.

in case of necessity, it remains possible to weight directly some constraints into a given construction, overriding thus the default weight assigned to the constraint types.


These three indicators do not have the same impact in the evaluation of the characterization, they are then balanced with coefficients in the normalized formula: • P I = (k×QI)+(l×SR)+(m×CC) As such, P I constitutes an evaluation of the characterization for a given construction.

it is necessary to take into account the "quality" of the constituents of the construction as well.


In the end, the GI index of the VP is better than that of the ill-formed NP: For the same reasons, the higher level construction S also compensates the bad score of the NP.

in the end, the final GI of the sentence is much lower than that of the corresponding wellformed sentence (see above The different figures of the sentence (2b) show that the violation of a unique constraint (in this case the Oblig(Adj) indicating the absence of the head in the AP) can lead to a global lower GI than the violation of two heavy constraints as for (2a).


Typically CRFs use binary indicator functions as features; these functions are only active when the observations meet some criteria and the label a t (or label pair, (a t−1 , a t )) matches a pre-specified label (pair).

in our model the labellings are word indices in the target sentence and cannot be compared readily to labellings at other sites in the same sentence, or in other sentences with a different length.


The final results of 6.47 and 5.19 with and without Model 4 features both exceed the performance of Model 4 alone.

the unsuper- vised Model 4 did not have access to the wordalignments in our training set.


(2005) who presented a word matching model for discriminative alignment which they they were able to solve optimally.

their model is limited to only providing one-to-one alignments.


Existing methods for exploiting comparable corpora look for parallel data at the sentence level.

we believe that very non-parallel corpora have none or few good sentence pairs; most of their parallel data exists at the sub-sentential level.


It requires the ability to recognize translational equivalence in very noisy environments, namely sentence pairs that express different (although overlapping) content.

a good solution to this problem would have a strong impact on parallel data acquisition efforts.


With this approach, one would need to obtain a corpus in which each ambiguous word has been manually annotated with the correct sense, to serve as training data.

supervised WSD systems faced an important issue of domain dependence when using such a corpus-based approach.


In our eariler work (Chan and Ng, 2005b), the posterior probabilities assigned by a naive Bayes classifier are used by the EM procedure described in the previous section to estimate the sense priors in a new dataset.

it is known that the posterior probabilities assigned by naive Bayes are not well calibrated (Domingos and Pazzani, 1996).


BC was built as a balanced corpus and contains texts in various categories such as religion, fiction, etc.

the focus of the WSJ corpus is on financial and business news.


We did not include the results from GI03 in the tables since the system is only applicable to part-of relations and we did not reproduce it.

the authors evaluated their system on a sample of the TREC-9 dataset and reported 83% precision and 72% recall (this algorithm is heavily supervised.)


Experimental results, for all relations and the two different corpus sizes, show that ESP-greatly outperforms the other methods on precision.

without the use of generic patterns, the ESP-system shows lower recall in all but the production relation.


When the percentage of labeled data increases from 50% to 100%, LP Cosine is still comparable to SVM in F-measure while LP JS achieves slightly better F-measure than SVM.

lP JS consistently outperforms lP Cosine .


This allows the grammar writer to specify any partial information about the signature, and provides the needed mathematical and computational capabilities to integrate the information with the rest of the signature.

this work does not define modules or module interaction.


Consider now the appropriateness relation.

to type signatures, Ap is not required to be a function.


Since Charniak's parser does its own tagging, this experiment did not examine the utility of prosodic disjuncture marks.

the combination of daughter annotation and -UNF propagation does lead to a better grammar-based reparandum-finder than parsers trained on flattened EDITED regions.


This paper uses data-driven n-gram user simulations (Georgila et al., 2005a) and a richer dialogue context.

increasing the size of the state space for RL has the danger of making the learning problem intractable, and at the very least means that data is more sparse and state approximation methods may need to be used (Henderson et al., 2005).


In our case we see that certain turns are rejected less than expected (row 3), while uncertain turns are rejected more than expected (row 4).

there is no interaction between neutral turns and rejections or between mixed turns and rejections.


We find that 'Certain' turns have less SRP than expected (in terms of AsrMis and Rej).

'Uncertain' turns have more SRP both in terms of AsrMis and Rej.


This constraint is expressed in a grammar G encoded as a regular expression ).

in order to cope with the prediction errors of the classifier, we approximate with an -gram language model on sequences of the refined tag labels: In order to estimate the conditional distribution e !


Dialog structure information is necessary for language generation (predicting the agents' response) and dialog state specific text-to-speech synthesis.

there are several challenging problems that remain to be addressed.


These models are trained to encourage nearby data points to have the same class label, and they can obtain impressive accuracy using a very small amount of labeled data.

since they model pairwise similarities among data points, most of these approaches require joint inference over the whole data set at test time, which is not practical for large data sets.


To see why, note that the entropy regularizer can be seen as a composition, , is given by (Boyd and Vandenberghe 2004)  As (2) is not concave, many of the standard global maximization techniques do not apply.

one can still use unlabeled data to improve a supervised CRF via iterative ascent.


These training criteria have yielded excellent results for various tasks.

real world tasks are evaluated by task-specific evaluation measures, including non-linear measures such as Fscore, while all of the above criteria achieve optimization based on the linear combination of average accuracies, or error rates, rather than a given task-specific evaluation measure.


The most probable outputŷ is given byŷ = arg max y∈Y p(y|x; λ).

z λ (x) never affects the decision ofŷ since z λ (x) does not depend on y.


It may be feared that since the objective function is not differentiable everywhere for ψ = ∞, problems for optimization would occur.

it has been shown (Le Roux and McDer-mott, 2005) that even simple gradient-based (firstorder) optimization methods such as GPD and (approximated) second-order methods such as Quick-Prop (Fahlman, 1988) and BFGS-based methods have yielded good experimental optimization results.


We expanded the basic features by using bigram combinations of the same types of features, such as words and part-of-speech tags, within window size 5.

to the above, we used the original feature set for NER.


Note that all above-mentioned systems are based on the assumption that the true quality of essays must be defined by human judges.

bennet and bejar (1998) have criticized the overreliance on human ratings as the sole criterion for evaluating computer performance because ratings are typically based as a constructed rubric that may ultimately achieve acceptable reliability at the cost of validity.


On one hand, we associate the words in the left half with food or cooking.

we associate those in the right half with animals or birds.


In essence, decision lists could be learned from a corpus consisting of a general corpus and the feedback corpus.

since the size of the feedback corpus is normally far smaller than that of general corpora, so is the effect of the feedback corpus on .


Thus, only a little improvement is expected in recall however much feedback corpus data become available.

most of the 13 Indeed, words around the target noun were effective.


Incorrect substitutions and newly injected erroneous material anywhere in the sentence counted as New Errors, even if the proposed replacement were otherwise correct.

changes in upper and lower case and punctuation were ignored.


The difference between τ c+ and τ c is expected.

in the next section this will be contrasted with the increased burden on the parser for τ c+ , since it is also responsible for selecting the correct dependency type for each arc among as many as 2 • |R| types instead of |R|.


the subject is RESTAURANT and verb is has, whereas the learned DSyntSs often place the attribute in subject position as a definite noun phrase.

the learned DSyntS can be incorporated into SPaRKy using the semantic representations to substitute learned DSyntSs into nodes in the sentence plan tree.


Synonymy, hypernymy or meronymy fall clearly in this latter category, and well known resources like WordNet (Miller, 1995), EuroWordNet (Vossen, 1998) or MindNet (Richardson et al., 1998) contain them.

as various researchers have pointed out (Harabagiu et al., 1999), these networks lack information, in particular with regard to syntagmatic associations, which are generally unsystematic.


The network of topical co-occurrences built from Topical Units is a subset of the initial network.

it also contains co-occurrences that are not part of it, i.e.


For us, supertagging decreases the speed slightly, because additional constraints means more work for the parser, and because our supertagger-parser integration is not yet optimal.


The mean number of relevant documents per query is 137 and the median is 81 (the most prolific query has 968 relevant documents).

each document is relevant to, on average, 1.11 queries (the median is 5.5 and the most generally relevant document is relevant to 20 different queries).


Our model is similar in spirit to the randomwalk summarization model (Otterbacher et al., 2005).

our model has several advantages over this technique.


Lapata (2002) examines the task of expressing the implicit relations in nominalizations, which are noun compounds whose head noun is derived from a verb and whose modifier can be interpreted as an argument of the verb.

with this work, our algorithm is not restricted to nominalizations.


This great expressiveness has the disadvantage that the parsing problem becomes N P-complete and cannot be solved efficiently.

good success has been achieved with transformation-based solution methods that start out with an educated guess about the optimal tree and use constraint failures as cues where to change labels, subordinations, or lexical readings.


As with many parsers, the attachment of prepositions poses a particular problem for the base WCDG of German, because it is depends largely upon lexicalized information that is not widely used in its constraints.

such information can be automatically extracted from large corpora of trees or even raw text: prepositions that tend to occur in the vicinity of specific nouns or verbs more often than chance would suggest can be assumed to modify those words preferentially (Volk, 2002).


Therefore, external evidence is either used to restrict the space of possibilities for a subsequent component (Clark and Curran, 2004) or to choose among the alternative results which a traditional rule-based parser usually delivers (Malouf and van Noord, 2004).

to these approaches, our system directly integrates the available evidence into the decision procedure of the rule-based parser by modifying the objective function in a way that helps guiding the parsing process towards the desired interpretation.


Natural language parsing is a hard task, partly because of the complexity and the volume of information that have to be taken into account about words and syntactic constructions.

it is necessary to have access to such information, stored in resources such as lexica and grammars, and to try and minimize the amount of missing and erroneous information in these resources.


Lease and Charniak (2005) use the Charniak parser for biomedical data and find that the use of out-of-domain trees and in-domain vocabulary information can considerably improve performance.

the work which is most directly comparable to ours is that of (Ratnaparkhi, 1999;Hwa, 1999;Gildea, 2001;Bacchiani et al., 2006).


Lexical verb classes have been used to support various (multilingual) tasks, such as computational lexicography, language generation, machine translation, word sense disambiguation, semantic role labeling, and subcategorization acquisition (Dorr, 1997;Prescher et al., 2000;Korhonen, 2002).

large-scale exploitation of the classes in real-world or domain-sensitive tasks has not been possible because the existing classifications, e.g.


annotated data (or a comprehensive list of verb senses) exists for the domain.

examination of a number of corpus instances suggests that the use of verbs is fairly conventionalized in our data 13 .


Most of the acquisition methods are based on distributional hypothesis (Harris, 1985), which states that semantically similar words share similar contexts, and it has been experimentally shown considerably plausible.

whereas many methods which adopt the hypothesis are based on contextual clues concerning words, and there has been much consideration on the language models such as Latent Semantic Indexing (Deerwester et al., 1990) and Probabilistic LSI (Hofmann, 1999) and synonym acquisition method, almost no attention has been paid to what kind of categories of contextual information, or their combinations, are useful for word featuring in terms of synonym acquisition.


Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words.

the naïve nearestneighbour approach to comparing context vectors extracted from large corpora scales poorly (O(n 2) in the vocabulary size).


Their paper concluded that recognizing the sub-events that comprise a single news event is essential for producing better summaries.

it is difficult to automatically break a news topic into sub-events.


Apparently, named entities are key elements in their model.

the constraints defining events seemed quite stringent.


We are aware of the important roles of information fusion and sentence compression in summary generation.

the focus of this paper is to evaluate event-based approaches in extracting the most important sentences.


Collecting human judgements is the method of choice for evaluating sentence compression models.

human evaluations tend to be expensive and cannot be repeated frequently; furthermore, comparisons across different studies can be difficult, particularly if subjects employ different scales, or are given different instructions.


The SSA does not correlate on both corpora with human judgements; it thus seems to be an unreliable measure of compression performance.

the Fscore correlates significantly with human ratings, yielding a correlation coefficient of r = 0.575 on the Ziff-Davis corpus and r = 0.532 on the Broadcast news.


When no parallel corpora are available the parameters can be manually tuned to produce compressions.

the supervised decision-tree model is not particularly robust on spoken text, it is sensitive to the nature of the training data, and did not produce adequate compressions when trained on the humanauthored Broadcast News corpus.


Barzilay (2002) has provided empirical evidence that proper order of extracted sentences improves their readability significantly.

ordering a set of sentences into a coherent text is a nontrivial task.


Several strategies to determine sentence ordering have been proposed as described in section 2.

the appropriate way to combine these strategies to achieve more coherent summaries remains unsolved.


This fact shows that integration of CHR experts with other experts worked well by pushing poor ordering to an acceptable level.

a huge gap between AGL and HUM orderings was also found.


Inter-annotator agreement is too low on fine-grained judgments.

for the coarse-grained judgments of more than or less than a day, and of approximate agreement on temporal unit, human agreement is acceptably high.


The main reason is that the understanding of the basic entailment processes will allow us to model more accurate semantic theories of natural languages (Chierchia and McConnell-Ginet, 2001) and design important applications (Dagan and Glickman, 2004), e.g., Question Answering and Information Extraction.

previous work (e.g., (Zaenen et al., 2005)) suggests that determining whether or not a text T entails a hypothesis H is quite complex even when all the needed information is explicitly asserted.


These readings are all semantically equivalent to each other.

the USR for (2) has 480 readings, which fall into two classes of mutually equivalent readings, characterised by the relative scope of "the lee of" and "a small hillside."


However, even first-order equivalence is an undecidable problem, and broadcoverage semantic representations such as those computed by the ERG usually have no welldefined model-theoretic semantics and therefore no concept of semantic equivalence.

we do not need to solve the full semantic equivalence problem, as we only want to compare formulas that are readings of the same sentence, i.e.


One way to formalise this is to enumerate exactly one representative of each equivalence class.

after such a step we would be left with a collection of semantic representations rather than an USR, and could not use the USR for ruling out further readings.


This is an improvement over our earlier algorithm (Koller and Thater, 2006), which computed a chart with four configurations for the graph in which 1 and 2 are existential and 3 is universal, as opposed to the three equivalence classes of this graph's configurations.

the present algorithm still doesn't achieve complete reduction for all USRs.


Indeed, this is the approach we use for our main results in Section 3.

because of the second problem noted above, in Section 4, we simulated the context by filling the cache with rules from the correct tree.


Significant results were obtained both for the Within model (N = 24, Z = 1.67, p < .05, one-tailed) and for the Copy model (N = 24, Z = 4.27, p < .001, onetailed).

the effect was much larger for the Copy model, a conclusion which is confirmed by comparing the differences of differences between the two models (N = 24, Z = 4.27, p < .001, one-tailed).


The increase in recall is statistically significant, and it shows classifier stacking can improve performance.

we did not find metaclassification and simple voting very effective.


Our grammars automatically learn the kinds of linguistic distinctions exhibited in previous work on manual tree annotation.

our grammars are much more compact and substantially more accurate than previous work on automatic annotation.


We would like to see only one sort of this tag because, despite its frequency, it always produces the terminal comma (barring a few annotation errors in the treebank).

we would expect to find an advantage in distinguishing between various verbal categories and NP types.


To prevent oversplitting, we could measure the utility of splitting each latent annotation individually and then split the best ones first.

not only is this impractical, requiring an entire training phase for each new split, but it assumes the contributions of multiple splits are independent.


This approach gives parsing accuracies of up to 90.7% on the development set, substantially higher than previous symbol-splitting approaches, while starting from an extremely simple base grammar.

in general, any automatic induction system is in danger of being entirely uninterpretable.


In the limit, each word may well have its own unique syntactic behavior, especially when, as in modern parsers, semantic selectional preferences are lumped in with traditional syntactic trends.

in practice, and given limited data, the relationship between specific words and their syntactic contexts may be best modeled at a level more fine than POS tag but less fine than lexical identity.


Our findings suggest that in the supervised setting the results of the direct and indirect approaches are comparable.

addressing directly the binary classification task has practical advantages and can yield high precision values, as desired in precision-oriented applications such as IR and QA.


In general, the classification performance of one-class approaches is usually quite poor, if compared to supervised approaches for the same tasks.

in many practical settings oneclass learning is the only available solution.


Although they are all based on the Bayesian model, Qin and Wang (2005) used an ensemble classifier.

the difference of the average value is not remarkable.


When we simply apply first-order semi-CRFs, we must distinguish states that have different previous states.

when we want to distinguish only the preceding named entity tags rather than the immediate previous states, feature forests can represent these events more compactly (Figure 4).


The result of the preceding entity information improves the performance.

the system with preceding information is not significantly better than the system without it 5 .


The contribution of the non-local information introduced by our method was not significant in the experiments.

other types of nonlocal information have also been shown to be effective (Finkel et al., 2005) and we will examine the effectiveness of other non-local information which can be embedded into label information.


Figure 2 contains an example of such a case: the cascade model will have to predict the type of the entire phrase Donna Karan International, in the context 'Since <chunk> went public in ..', which will give it a better opportunity to classify it as an organization.

because the joint model and AIO have a word view of the sentence, will lack the benefit of examining the larger region, and will not have access at features that involve partial future classifications (such as the fact that another mention of a particular type follows).


Among all the issued extractions, the larger of the number of incorrect extractions is, the closer the extraction redundancy for that document is to 1.

the extraction redundancy can never be 1 according to our definition, since this measure is only defined over the documents that contain at lease one correct extraction.


To support machine translation, parallel sentences should be extracted from the mined parallel documents.

current sentence alignment models, (Brown et al 1991;Gale & Church 1991;Wu 1994;Chen 1993;Zhao and Vogel, 2002;etc.)


Compared to Table 1, the results show a significant improvement of over 10% on the baseline f-score for questions.

the tests on the non-question Section 23 data show not only a significant drop in accuracy but also a drop in coverage.


For example when trained on only 10% of the 3600 questions used in this experiment, the parser successfully parses all of the 400 question test set and achieves an fscore of 85.59.

the results for the tests on WSJ Section 23 are considerably worse.


Free-word order languages typically pose greater challenges for syntactic theories (Rambow, 1994), and the richer inflectional morphology of these languages creates additional problems both for the coverage of lexicalized formalisms such as CCG or TAG, and for the usefulness of dependency counts extracted from the training data.

formalisms such as CCG and TAG are particularly suited to capture the cross-ing dependencies that arise in languages such as Dutch or German, and by choosing an appropriate linguistic representation, some of these problems may be mitigated.


Compared with word-based SMT systems, phrase-based systems can easily address reorderings of words within phrases.

at the phrase level, reordering is still a computationally expensive problem just like reordering at the word level (Knight, 1999).


Performance gains have been reported for systems with lexicalized reordering model.

since reorderings are related to concrete phrases, researchers have to design their systems carefully in order not to cause other problems, e.g.


A simple way to compute this probability is to take counts from the training data and then to use the maximum likelihood estimate (MLE) The similar way is used by lexicalized reordering model.

in our model this way can't work because blocks become larger and larger due to using the merging rules, and finally unseen in the training data.


When decoding a speech signal, words are generated in the same order in which their corresponding acoustic signal is consumed.

that is not necessarily the case in MT due to the fact that different languages have different word order requirements.


The paper suggests that reordering the input reduces the translation error rate.

it does not provide a methodology on how to perform this reordering.


In the absence of richer models such as the proposed distortion model, our results suggest that it is best to decode monotonically and only allow local reorderings that are captured in our phrase dictionary.

when the distortion model is used, we see statistically significant increases in the BLEU score as we consider more word reorderings.


In particular, it is possible to deal with it even when no bilingual resources are available.

when it is possible to exploit bilingual repositories, such as a synset-aligned WordNet or a bilingual dictionary, the obtained performance is close to that achieved for the monolingual task.


et al., 2005) which are the two general approaches to personalized search.

(J. Pitkow et al., 2002) to the best of our knowledge, how to exploit these two types of implicit feedback in a unified way, which not only brings collaboration between query expansion and result re-ranking but also makes the whole system more concise, has so far not been well studied in the previous work.


In Figure 5, we can see that the precision of "PAIR No QE" is better than that of UCAIR among top 5 and top 10 documents, and is almost the same as that of UCAIR among top 20 and top 30 documents.

pAIR is much better than UCAIR in all measurements.


Compared with dictionary or corpus based methods, the advantage of MT-based query translation lies in that technologies integrated in MT systems, such as syntactic and semantic analysis, could help to improve the translation accuracy (Jones et al., 1999).

in a very long time, fewer experiments with MT-based methods have been reported than with dictionary-based methods or corpus-based methods.


Thus, in practice the rate of growth on the x-axis of Figure 1 will slow as the corpus size increases.

the number of documents (shown on the y-axis in Figure 1) remains unbounded.


For example, one study found that full-text articles require weighting schemes that consider document length (Kamps, et al, 2005).

controlling the weights for document lengths may hide a systematic difference between the language used in abstracts and the language used in the body of a document.


substrings that are common enough to be observed on training data.

a key limitation of phrase-based models is that they fail to model reordering at the phrase level robustly.


Most previous studies of the NER of speech data used generative models such as hidden Markov models (HMMs) (Miller et al., 1999;Palmer and Ostendorf, 2001;Horlock and King, 2003b;Béchet et al., 2004;Favre et al., 2005).

in text-based NER, better results are obtained using discriminative schemes such as maximum entropy (ME) models (Borthwick, 1999;Chieu and Ng, 2003), support vector machines (SVMs) (Isozaki and Kazawa, 2002), and conditional random fields (CRFs) (McCallum and Li, 2003).


If all the candidates are classified negative, TA is judged nonanaphoric.

the preference-based approach (Yang et al., 2003;Iida et al., 2003) decomposes the task into comparisons of the preference between candidates and selects the most preferred one as the antecedent.


The larger is Ò, the larger corpus is required to avoid data sparseness.

though loworder Ò-grams do not suffer from data sparseness severely, they do not reflect the language characteristics well, either.


All the experiments above considered left context only.

kang reported that the probabilistic model using both left and right context outperforms the one that uses left context only (kang, 2004).


Even though word spacing is one of the important tasks in Korean information processing, it is just a simple task in many other languages such as English, German, and French.

due to its generality, the importance of the proposed method yet does hold in such languages.


The solution for a maximization problem can be found using an exhaustive search method.

the complexity is very high in practice for a large number of pairs to be processed.


Interestingly, Buckley et al., (2000) points out that "English query words are treated as potentially misspelled French words" and attempts to treat English words as variations of French words according to lexicographical rules.

when two languages are very distinct, e.g., English-Korean, English-Chinese, transliteration from English words is utilized for cognate matching.


Figure  1 shows the results of the experiments done for different alignment score cut-off without considering the Frequency Range constraint on the three corpora.

it was observed that the performance of the algorithm reduced slightly on introducing this BASS filter.


The starting point constraint expresses range in terms of number of words.

it has been observed (see section 2.2) that the optimum value of the range varies with the nature of text.


The paucity of the resources suggests that statistical techniques are not suitable for the task.

lexicon-based approaches are highly resource-dependent.


Existing methods that are used to extract loanwords from Korean corpora (Myaeng and Jeong, 1999;Oh and Choi, 2001) use the phonetic differences between conventional Korean words and loanwords.

these methods require manually tagged training corpora, and are expensive.


In phrase (a), there is no inflection, and the suffix is easily segmented.

in phrase (b), a consonant insertion has occurred.


If both of these are consonants, we determine that a vowel was eliminated.

a number of nouns end with two consonants inherently, and therefore, we referred to a textbook on Mongolian grammar (Bayarmaa, 2002) to produce 12 rules to determine when to insert a vowel between two consecutive consonants.


For example, if any of "м", "г", "л", "б", "в", or "р" are at the end of a noun, a vowel is inserted.

if any of "ц", "ж", "з", "с", "д", "т", "ш", "ч", or "х" are the second to last consonant in a noun, a vowel is not inserted.


We used the Hepburn system, because its representation is similar to that used in Mongolian, compared to the Kunrei system.

we adapted 11 Mongolian romanization expressions to the Japanese Hepburn romanization.


Because the N-gram retrieval method does not consider the order of the characters in a target word, the accuracy of matching two words is low, but the computation time is fast.

because DP matching considers the order of the characters in a target word, the accuracy of matching two words is high, but the computation time is slow.


Second, the factorization into four separate steps makes it theoretically possible to modify each step independently in order to investigate the effects of the various modeling assumptions.

the mathematical statement of the model and the approximations necessary for the search procedure make it unclear how to modify the model in any interesting way.


Morphological Behavior Classes: The MBCs are variant-independent, so in theory no changes needed to be implemented.

as Levantine is our first dialect, we expand the MBCs to include two AMs not found in MSA: the aspectual particle and the postfix negation marker.


Previous studies have shown that allowing the parser to resolve POS tag ambiguity does not improve performance.

for grammar formalisms which use more fine-grained grammatical categories, for example TAG and CCG, tagging accuracy is much lower.


This leaves the parser the task of managing the very large parse space resulting from the high degree of lexical category ambiguity (Hockenmaier and Steedman, 2002;Hockenmaier, 2003).

one of the original motivations for supertagging was to significantly reduce the syntactic ambiguity before full parsing begins (Bangalore and Joshi, 1999).


This is because at the lowest levels of ambiguity the extra POS tags can be treated as being of similar reliability.

at higher levels of ambiguity many POS tags are added which are unreliable and should not be trusted equally.


suffixes and character types) of the unknown words.

this approach has limitations in available information.


The optimal Λ can be obtained by quasi-Newton methods using the above L Λ and ∂L Λ ∂λ i,j , and we use L-BFGS (Liu and Nocedal, 1989) for this purpose 2 .

the calculation is intractable because Z Λ (w l ) (see Equation 9)in Equation (16) and a term in Equation 17contain summations over all the possible POS tags.


This procedure is iterated for all the words in the corpus.

this approach is not applicable to our experiments because those words that appear only once in the corpus do not have global information and are useless for learning the global model, so we use the two-fold cross validation method.


Most speech recognition systems perform well when trained for a particular accent (Lawson et al., 2003).

with call centers now being located in different parts of the world, the requirement of handling different ac-cents by the same speech recognition system further increases word error rates.


In the absence of tagged dataset we could not quantify our observation.

when we compared the automatically generated topic specific information to the extracted information from the hand labeled calls, we noted that almost all the issues have been captured.


Being able to bootstrap more training data is of course very useful.

we need to dig deeper to investigate how the increase in data affected the machine learning.


We assume the one with the higher confidence is chosen.

we don't have enough data to reliably estimate rule confidences for the original GTag rules; so, for the purposes of VerbOcean rule integration, we assigned either the original Ver-bOcean rules as having greater confidence than the original GTag rules in case of a conflict (i.e., a preference for the more specific rule), or viceversa.


Discriminative learning from reference translations is inherently problematic because standard discriminative methods need to know which outputs are correct and which are not.

a proposed translation that differs from a reference translation need not be incorrect.


In principle, w could have been tuned by maximizing conditional probability or maximizing margin.

these two options require either marginalization or numerical optimization, neither of which is tractable over the space of output sentences y and correspondences h. In contrast, the perceptron algorithm requires only a decoder that computes f (x; w).


The problem with (c) is that the correspondence h contains an incorrect alignment (', a).

since h is unobserved, the training procedure has no way of knowing this.


(2005) compared their results with Model 4 using "intersection" by looking at AER (with the "Sure" versus "Possible" link distinction), and restricted themselves to considering 1-to-1 alignments.

"union" and "refined" alignments, which are many-to-many, are what are used to build competitive phrasal SMT systems, because "intersection" performs poorly, despite having been shown to have the best AER scores for the French/English corpus we are using (Och and Ney, 2003).


In this paper, we propose a measure of similarity to capture this intuition.

to anchoring, our second algorithm, called the clustering approach, takes a top-down view.


Current approaches often employ machine learning techniques and require supervised data.


Most successful approaches to NER employ machine learning techniques, which require supervised training data.

for many languages, these resources do not exist.


Moreover, it is often difficult to find experts in these languages both for the expensive annotation effort and even for language specific clues.

comparable multilingual data (such as multilingual news streams) are becoming increasingly available (see section 4).


Transliteration based approaches require a good model, typically handcrafted or trained on a clean set of transliteration pairs.

time sequence similarity based approaches would incorrectly match words which happen to have similar time signatures (e.g., Taliban and Afghanistan in recent news).


In turn, the chosen positive examples contain other characteristic substring pairs, which will be used by the model to select more positive examples on the next round, and so on.

if the initial set is too small, too few of the characteristic transliteration features are extracted to select a clean enough training set on the next round of training.


Moreover, the difference between our method and Bunescu and Mooney (2005) is that their kernel is defined on the shortest path between two entities instead of the entire subtrees.

the path does not maintain the tree structure information.


The small form factor of such devices limits the amount of text that can be displayed.

conciseness exists in tension with completeness.


In particular contexts, it contributes also to the induction of the entailment relation between win and play, as John McEnroe has the property of playing.

as the example shows, classes relevant for acquiring selectional preferences (such as human) are explicit, as they do not depend from the context.


Indeed, this latter property may be relevant only in the context of the previous sentence.

there is a way to overcome this limitation: agentive nouns such as runner make explicit this kind of property and often play subject roles in sentences.


it may happen that these correctly detected entailments are accidental, that is, the detected relation is only valid for the given text.


1 reports the happens-before lexico-syntactic patterns (P hb ) as proposed in (Chklovski and Pantel, 2004).

to what is done in (Chklovski and Pantel, 2004) we decided to directly count patterns derived from different verbal forms and not to use an estimation factor.


The results for the "Adventure" corpus are in general better than the results for the "Thief" corpus.

this is due to the "Thief" corpus being smaller and having an infrequent number of "Excellent" and "Poor" stories, as shown in Table 1.


Parallelization might not uniformly reduce training time because different label classifiers train at different rates.

parallelization uniformly reduces memory usage because each label classifier trains only on inferences whose consequent item has that label.


To our knowledge, most top ranked QA systems in TREC are supported by effective NER modules which may identify and classify more than 20 types of named entities (NE), such as abbreviation, music, movie, etc.

developing such named entity recognizer is not trivial.


This may be also used to explain why CorME outperforms ApprMatch in Table 1.

removing answer re-ranking doesn't affect much.


Between the heads of two argument structures there can exist lexical chains of size 0, meaning that the heads of the two structures are in the same synset.

the type of the start structure can be different than the type of the target structure.


Computational systems that learn to transform natural language sentences into formal meaning representations have important practical applications in enabling user-friendly natural language communication with computers.

most of the research in natural language processing (NLP) has been focused on lower-level tasks like syntactic parsing, word-sense disambiguation, information extraction etc.


It is difficult for rule-based methods or even statistical featurebased methods to capture the full range of NL contexts which map to a semantic concept because they tend to enumerate these contexts.

kernel methods allow a convenient mechanism to implicitly work with a potentially infinite number of features which can robustly capture these range of contexts even when the data is noisy.


In each iteration, the positive examples from the previous iteration are first removed so that new positive examples which lead to better correct derivations can take their place.

negative examples are accumulated across iterations for better accuracy because negative examples from each iteration only lead to incorrect derivations and it is always good to include them.


Similarly, in the task of automatic routing of customer emails and automatic answering of some of these, the detection of threats of legal action could be useful.

systems that use cue phrases usually rely on manually compiled lists, the acquisition of which is time-consuming and error-prone and results in cue phrases which are genre-specific.


frame usage improves the generalization ability of the learning algorithm).

the results obtained without the frame information are very poor.


The expected behaviour may only be achieved if : • the system knows in which volume the search is to be performed, • the system knows where, in the volume entry, the headword is to be found, • the system is able to produce a presentation for the retrieved XML structures.

as the Jibiki platform is entirely independent of the underlying dictionary structure <volume-metadata [...] dbname="lexalpfra" dictname="LexALP" name="LexALP_fra" source-language="fra"> <cdm-elements> <cdm-entry-id index="true" xpath="/volume/entry/@id"/> <cdm-headword d:lang="fra" index="true" xpath="/volume/entry/term/text()"/> <cdm-pos d:lang="fra" index="true" xpath="/volume/entry/grammar/text()"/> xhtml"/> </volume-metadata> Figure 9: Excerpt of a volume descriptor (which makes it highly adaptable), the expected result may only be achieved if additional metadata is added to the system.


After each update, the resulting XML structure is stored in the dictionary database.

it is not available to other users until it is marked as finished by the contributor (by clicking on the save button).


We evaluated translation quality using a relatively simple translation system.

more sophisticated systems can draw equal benefit from the same lexical resources.


This approach is based on the simple assumption that if two words are mutual translations, then their most frequent collocates are likely to be mutual translations as well.

the approach requires large comparable corpora, the collection of which presents non-trivial challenges.


The operational value of these keyword phrases was determined by the access they provide to video segments in a large archive of oral histories.

our technique is not limited to this application.


The hybrid method we developed relies on the parser Fips (Wehrli, 2004), that implements the Government and Binding formalism and supports several languages (besides the ones mentioned in 2 "Ideally, in order to identify lexical relations in a corpus one would need to first parse it to verify that the words are used in a single phrase structure.

in practice, freestyle texts contain a great deal of nonstandard features over which automatic parsers would fail.


A rule like (e) is particularly unfortunate, since it allows the word were to be added without any other evidence that the VP should be in passive voice.

the composed-rule derivation of C 4 incorporates more linguistic evidence in its rules, and re-orderings are motivated by more syntactic context.


Also, on the one hand, our AlTemp system represents quite mature technology, and incorporates highly tuned model parameters.

our syntax decoder is still work in progress: only one model was used during search, i.e., the EM-trained root-normalized SBTM, and as yet no language model is incorporated in the search (whereas the search in the AlTemp system uses two phrase-based translation models and 12 other feature functions).


These were used to model generic word-alignment patterns such as noun-adjective re-ordering between English and French (Och, 1998).

we induce fine-grained partitions of the lexicon, conceptually closer to automatic lemmatisation, optimised specifically to assign translation probabilities.


(2004) used an MRF to embody hard constraints within semi-supervised clustering.

we use an iterative EM algorithm to learn soft constraints within the 'prior' monolingual space based on the results of clustering with bilingual statistics.


The disparate elements of such constituents would usually be aligned to the same word in a translation.

when our hierarchical aligner saw two words linked to one word, it ignored one of the two links.


This statement is consistent with our findings.

most of the knowledge loss could be prevented by allowing a gap.


Zhang and Gildea (2004) found that their alignment method, which did not use external syntactic constraints, outperformed the model of Yamada and Knight (2001).

yamada and Knight's model could explain only the data that would pass the nogap test in our experiments with one constraining tree (first column of Table 3).


There is in general no known analytic form for the density of PY(d, θ, G 0 ) when the vocabulary is finite.

this need not deter us as we will instead work with the distribution over sequences of words induced by the Pitman-Yor process, which has a nice tractable form and is sufficient for our purpose of language modelling.


Chat language text appears frequently in chat logs of online education (Heard-White, 2004), customer relationship management (Gianforte, 2003), etc.

wed-based chat rooms and BBS systems are often abused by solicitors of terrorism, pornography and crime (McCullagh, 2004).


Performance equivalent to the methods in existence is achieved consistently.

the issue of normalization is addressed in their work.


This proves that phonetic mapping models used in XSCM are helpful in addressing the dynamic problem.

quality of XSCM in this experiment still drops by 0.05 on the six time-varying test sets.


In these statistical models, language models are essential for word segmentation disambiguation.

an uncom-pressed language model is usually too large for practical use since all realistic applications have memory constraints.


Compared to M1, M2 decreases 0.98% of F-score.

in the two web data sets (i.e., TW and CN), M2 is much better than M1.


Comparing Tables 4 and 6 shows that while partial output almost doubles coverage, this comes at a price of a severe drop in quality (BLEU score drops from 0.7147 to 0.5590).

comparing Tables 5 and 6 shows that lexical smoothing achieves a similar increase in coverage with only a very slight drop in quality.


(2005)    Using hand-crafted grammar-based generation systems (Langkilde-Geary, 2002;Callaway, 2003), it is possible to achieve very high results.

hand-crafted systems are expensive to construct and not easily ported to new domains or other languages.


Several GRE algorithms have addressed the issue of generating locative expressions (Dale and Haddock, 1991;Horacek, 1997;Gardent, 2002;Krahmer and Theune, 2002;Varges, 2004).

all these algorithms assume the GRE component has access to a predefined scene model.


We can apply a description like the circle near the square to either circle if none other were present.

if both are present we can interpret the reference based on relative proximity to the landmark the square.


Usually when unlucky errors occur, the system generates a reasonable query and an appropriate answer type, and at least one passage containing the right answer is returned.

there may be returned passages that have a larger number of query terms and an incorrect answer of the right type, or the query terms might just be physically closer to the incorrect answer than to the correct one.


If the scores associated with candidate answers (in both directions) were true probabilities, then a Bayesian approach would be easy to develop.


The Natural Language Generation (NLG) community has produced over the years a considerable number of generic sentence realization systems: Penman (Matthiessen and Bateman, 1991), FUF (Elhadad, 1991), Nitrogen (Knight and Hatzivassiloglou, 1995), Fergus (Bangalore and Rambow, 2000), HALogen (Langkilde-Geary, 2002), Amalgam (Corston-Oliver et al., 2002), etc.

when it comes to end-to-end, text-totext applications -Machine Translation, Summarization, Question Answering -these generic systems either cannot be employed, or, in instances where they can be, the results are significantly below that of state-of-the-art, application-specific systems (Hajic et al., 2002;Habash, 2003).


In the first post-hoc test, we found a significant difference between BEST and WORDS-TRI (t a V:H,p < I:VTxIH 12 ), indicating that there is room for improvement of our ranker.

in considering the top scoring feature sets, we did not find a significant difference between WORDS-TRI and WORDS-BI (t a P:Q, p < H:HPP), from which we infer that the difference among all of WORDS-TRI, ALL-BI, ALL-TRI and WORDS-BI is not significant also.


In this paper, we have presented a method for adapting a language generator to the strengths and weaknesses of a particular synthetic voice by training a discriminative reranker to select paraphrases that are predicted to sound natural when synthesized.

to previous work on this topic, our method can be employed with any speech synthesizer in principle, so long as features derived from the synthesizer's unit selection search can be made available.


MT and CLIR systems rely heavily on bilingual lexicons, which are typically compiled manually.

in view of the current information explosion, it is labor intensive, if not impossible, to compile a complete proper nouns lexicon.


Although word-sense disambiguation methods can be applied, these are not free of errors.

methods based on language-independent representation also have limitations.


(2000) aimed at annotating newswire text for analyzing temporal information.

these previous work are different from ours, because these work only dealt with newswire text including a lot of explicit temporal expressions.


They use the handcrafted dictionary and some inference rules to determine the time periods of events.

we do not resort to such a hand-crafted material, which requires much labor and cost.


We used YamCha 4 , the multi-purpose text chunker using Support Vector Machines, as an experimental tool.

any tagging direction and window sizes did not improve the performance of classification.


In future work, we will investigate minimal tree edit distance (Bille, 2005) and related formalisms which are defined on tree structures and can therefore model divergences explicitly.

it is an open question whether cross-linguistic syntactic analyses are similar enough to allow for structure-driven computation of alignments.


For example, the correct answers combination to questions showed in figure-1 is "August 12; 118; Barents Sea".

there is also a combination of "Aug. 12, two; U.S." which has higher pointwise mutual information due to the frequently occurred noisy information of "two U.S. submarines" and "two explosions in the area Aug. 12 at the time".


P (F = E) >> P (F = E), proper names with low counts then are encouraged to link to proper names during training; and consequently, conditional probability mass would be more focused on correct name translations.

names are discouraged to produce non-names.


The semantic constraints between "NULL" and any target words can be derived in the same way.

this is chosen for mostly computational convenience, and is not the only way to address the empty word issue.


The advantages of modeling how a target language syntax tree moves with respect to a source language syntax tree are that (i) we can capture the fact that constituents move as a whole and generally respect the phrasal cohesion constraints (Fox, 2002), and (ii) we can model broad syntactic reordering phenomena, such as subject-verb-object constructions translating into subject-object-verb ones, as is generally the case for English and Japanese.

there is also significant amount of information in the surface strings of the source and target and their alignment.


Syntactic methods are an increasingly promising approach to statistical machine translation, being both algorithmically appealing (Melamed, 2004;Wu, 1997) and empirically successful (Chiang, 2005;Galley et al., 2006).

despite recent progress, almost all syntactic MT systems, indeed statistical MT systems in general, build upon crude legacy models of word alignment.


Under certain precise conditions, as described in (Abney, 2004), we can analyze Algorithm 1 as minimizing the entropy of the distribution over translations of U .

this is true only when the functions Estimate, Score and Select have very prescribed definitions.


these were run on the smaller Eu-roParl corpus.


Experiments on the EuroParl corpus show a decrease in WER.

the selection algorithm applied there is actually supervised because it takes the reference translation into account.


matchWSD is then invoked for c 2 , which is aligned to only one chunk e 3 e 4 e 5 .

since this chunk has already been examined by c 1 with which it is considered as a phrase, no further matching is done for c 2 .


These approaches have shown good results; particularly those using supervised learning (see Mihalcea et al., 2004 for an overview of state-ofthe-art systems).

current approaches rely on limited knowledge representation and modeling techniques: traditional machine learning algorithms and attribute-value vectors to represent disambiguation instances.


Results in Figure 3 show that a-truePred starts off at a higher accuracy and performs consistently better than the a curve.

though a-truePrior starts at a high accuracy, its performance is lower than a-truePred and a after 50% of adaptation examples are added.


In (McCarthy et al., 2004), a method was presented to determine the predominant sense of a word in a corpus.

in (Chan and Ng, 2005), we showed that in a supervised setting where one has access to some annotated training data, the EMbased method in section 5 estimates the sense priors more effectively than the method described in (Mc-Carthy et al., 2004).


(2006) used active learning for 5 verbs using coarse-grained evaluation, and H. T. Dang (2004) employed active learning for another set of 5 verbs.

their work only investigated the use of active learning to reduce the annotation effort necessary for WSD, but 55 did not deal with the porting of a WSD system to a different domain.


Compared to the well known N-gram language models, discriminative language models can achieve more accurate discrimination because they can employ overlapping features and nonlocal information.

discriminative language models have been used only for re-ranking in specific applications because negative examples are not available.